We didn't buy the 'AI safety' narrative when it first surfaced in 2023. We saw it as a hedge — a way for well-capitalized labs to erect barriers to entry while posing as guardians of humanity. Perplexity Co-Founder Andy Konwinski just confirmed our thesis. His statement that "AI safety is an excuse to lock down frontier research" isn't a radical opinion. It's the logical conclusion of watching a handful of private laboratories turn ethical concern into a moat.
We've been here before. In 2017, I watched Waves Platform's ICO collapse under transaction fee spikes. The team had technical pedigree — a MS in Blockchain Engineering from a top school. But the infrastructure couldn't handle demand. I learned that day that technical correctness does not guarantee market viability. The same pattern repeats in AI today: the loudest safety advocates are the ones with the most to lose from competition.
Context: The Fable 5 Incident and the Power of Gatekeeping
Konwinski's criticism centers on Anthropic's Fable 5 event. In 2024, Anthropic internally flagged a research project deemed too risky to externalize. The team halted collaboration with outside researchers, citing safety protocols. On the surface, this seems responsible. Dig deeper, and the patterns emerge.
Anthropic controls access to its most powerful models, its compute clusters, and its research data. By defining 'safety' internally, they create a binary: either you accept their rules, or you are locked out. This isn't safety — it's infrastructure gatekeeping. The same dynamic plays out in blockchain: Layer2 teams control sequencers, VC-backed protocols fragment liquidity, and projects claim 'security audits' as a shield against scrutiny.
We didn't accept the Layer2 fragmentation narrative when VCs sold it as 'scaling.' We analyzed the on-chain data — same wallets, same addresses, shifted across chains. Liquidity wasn't expanding; it was being sliced. The result was a user experience nightmare and a handful of teams capturing fees.
Core: Dissecting the 'Safety' Argument — A Structural Analysis
Let's break down the AI safety argument using the same framework we apply to smart contract audits.
1. Claim: Safety requires centralized control. - Counterargument: Centralized control creates single points of failure. If one lab's safety definition is flawed, the entire system inherits that flaw. Decentralized oversight — like multiple audit firms reviewing the same code — provides redundancy. The same principle applies to AI alignment.
2. Claim: Only well-funded labs can afford safety research. - Counterargument: Safety research is a public good. When private labs monopolize it, they extract rent from the entire ecosystem. In DeFi, we saw the same: projects with VC backing claimed 'security' while leaving users exposed to oracle manipulation and reentrancy bugs. Code audits are hints, not guarantees.
3. Claim: Open models increase catastrophic risk. - Counterargument: Open models allow for external verification. The most dangerous AI systems are the ones we cannot inspect. In 2022, I watched Terra's collapse unfold because the team hid the collateralization ratios. When the market demanded transparency, the system failed. AI safety follows the same logic: transparency reduces systemic risk.
Based on my audit experience with Uniswap V2 in 2020, I learned that vulnerabilities are often hidden in assumptions — not code. The team assumed their yield aggregator was safe because they controlled the contract. I found a reentrancy path that would have drained 50 ETH. The same assumption is baked into closed AI labs: they assume their internal safety processes are sufficient because they control the pipeline. They are not.
Contrarian: The Real Risk Isn't AI Safety — It's Intelligence Monopolization
The contrarian angle is uncomfortable: the greatest threat to humanity isn't a rogue AGI. It's a small group of entities controlling the next generation of intelligence. History shows that monopolies of power — whether economic, technological, or political — inevitably lead to stagnation and abuse.
Consider the NFT market in 2021. When Bored Ape Yacht Club dominated, OpenSea enforced royalty enforcement. Then Blur entered, offering zero fees and no royalties. The market fragmented. Liquidity fled. Creators lost revenue. The OpenSea royalty surrender killed the creator economy. The same pattern repeats in AI: the so-called 'safety' labs are the royalty enforcers, and they are using ethics to extract value from the wider ecosystem.
Most retail investors and developers cheer for open-source AI without understanding the infrastructure underneath. They see Llama 3 and Mistral Large and think 'decentralization.' But look at the compute: who owns the training clusters? Who controls the data pipelines? The same VCs that funded the Layer2 fragmentation now fund AI safety research. We didn't fall for it then. We shouldn't fall for it now.
Takeaway: What to Watch and Where to Act
The Perplexity vs. Anthropic debate is more than a philosophical spat. It's a signal for where capital should flow — and where it should exit.
Exits: - Projects that tie 'safety' to exclusive API access or closed-source models. - Labs that refuse to publish independent audit trails of their safety protocols. - Any team that uses 'regulation' as a reason to centralize further.
Entries: - Decentralized compute networks that allow independent researchers to train models without gatekeeping. - Open-source initiatives that create verifiable proof-of-safety through community review. - Infrastructure that bridges AI with on-chain verification — smart contracts that enforce model transparency.
We didn't join the 2021 NFT mania at the top. We calculated the floor price premium against secondary volume and exited before the crash. We used that liquidity to buy undervalued Layer2 governance tokens, betting on technical scalability. The same discipline applies here. The AI safety narrative will peak when the market realizes it's a toll booth, not a shield.
The future of intelligence should not be decided by a handful of private laboratories. It should be built by a global community with transparent rules and open verification. We didn't become traders by trusting the hype. We became traders by trusting the code.
Let the market decide. But make sure you're trading on data, not narratives.